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Neural Network-Based Face Detection

Published: 01 January 1998 Publication History

Abstract

We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates.

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Published In

cover image IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence  Volume 20, Issue 1
January 1998
95 pages
ISSN:0162-8828
Issue’s Table of Contents

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IEEE Computer Society

United States

Publication History

Published: 01 January 1998

Author Tags

  1. Face detection
  2. artificial neural networks
  3. computer vision
  4. machine learning.
  5. pattern recognition

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